Improved Guarantees for Fully Dynamic k-Center Clustering with Outliers in General Metric Spaces

The metric k-center clustering problem with z outliers, also known as (k, z)-center clustering, involves clustering a given point set P in a metric space (M, d) using at most k balls, minimizing the maximum ball radius while excluding up to z points from the clustering. This problem holds fundamental significance in various domains, such as machine learning, data mining, and database systems. This paper addresses the fully dynamic version of the problem, where the point set undergoes continuous updates (insertions and deletions) over time. The objective is to maintain an approximate (k, z)-center clustering with efficient update times.